Overview

Dataset statistics

Number of variables17
Number of observations31
Missing cells48
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory152.3 B

Variable types

Text3
Numeric6
Categorical8

Dataset

Description부산광역시영도구_가로수_20221226
Author부산광역시 영도구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15064294

Alerts

구군명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 경도 and 5 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
식재거리(km) is highly overall correlated with 총합계 and 5 other fieldsHigh correlation
총합계 is highly overall correlated with 식재거리(km) and 6 other fieldsHigh correlation
왕벚나무 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
후박나무 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
은행나무 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
느티나무 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
이팝나무 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
먼나무 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
은행나무 is highly imbalanced (65.0%)Imbalance
느티나무 is highly imbalanced (69.4%)Imbalance
이팝나무 is highly imbalanced (55.7%)Imbalance
먼나무 is highly imbalanced (65.0%)Imbalance
해송 is highly imbalanced (79.4%)Imbalance
가시나무 is highly imbalanced (79.4%)Imbalance
왕벚나무 has 24 (77.4%) missing valuesMissing
후박나무 has 24 (77.4%) missing valuesMissing

Reproduction

Analysis started2023-12-10 16:37:31.970216
Analysis finished2023-12-10 16:37:37.150856
Duration5.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위치명
Text

Distinct18
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T01:37:37.265001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length13.419355
Min length13

Characters and Unicode

Total characters416
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)41.9%

Sample

1st row부산광역시 영도구 태종로
2nd row부산광역시 영도구 태종로
3rd row부산광역시 영도구 태종로
4th row부산광역시 영도구 태종로
5th row부산광역시 영도구 태종로
ValueCountFrequency (%)
부산광역시 31
33.3%
영도구 31
33.3%
태종로 7
 
7.5%
해양로 4
 
4.3%
절영로 3
 
3.2%
영선대로 2
 
2.2%
와치로 2
 
2.2%
남항새싹7길 1
 
1.1%
대교로 1
 
1.1%
산업로 1
 
1.1%
Other values (10) 10
 
10.8%
2023-12-11T01:37:37.661234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
62
14.9%
영 37
8.9%
산 32
7.7%
시 32
7.7%
부 31
7.5%
광 31
7.5%
역 31
7.5%
도 31
7.5%
구 31
7.5%
로 27
 
6.5%
Other values (33) 71
17.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 352
84.6%
Space Separator 62
 
14.9%
Decimal Number 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 37
10.5%
산 32
9.1%
시 32
9.1%
부 31
8.8%
광 31
8.8%
역 31
8.8%
도 31
8.8%
구 31
8.8%
로 27
7.7%
태 7
 
2.0%
Other values (30) 62
17.6%
Decimal Number
ValueCountFrequency (%)
7 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 352
84.6%
Common 64
 
15.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 37
10.5%
산 32
9.1%
시 32
9.1%
부 31
8.8%
광 31
8.8%
역 31
8.8%
도 31
8.8%
구 31
8.8%
로 27
7.7%
태 7
 
2.0%
Other values (30) 62
17.6%
Common
ValueCountFrequency (%)
62
96.9%
7 1
 
1.6%
2 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 352
84.6%
ASCII 64
 
15.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
62
96.9%
7 1
 
1.6%
2 1
 
1.6%
Hangul
ValueCountFrequency (%)
영 37
10.5%
산 32
9.1%
시 32
9.1%
부 31
8.8%
광 31
8.8%
역 31
8.8%
도 31
8.8%
구 31
8.8%
로 27
7.7%
태 7
 
2.0%
Other values (30) 62
17.6%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.085906
Minimum35.064082
Maximum35.095856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:37:37.823448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.064082
5-th percentile35.073149
Q135.082321
median35.08771
Q335.091955
95-th percentile35.095065
Maximum35.095856
Range0.031774
Interquartile range (IQR)0.009634

Descriptive statistics

Standard deviation0.0080616986
Coefficient of variation (CV)0.00022977028
Kurtosis0.20344971
Mean35.085906
Median Absolute Deviation (MAD)0.004804
Skewness-0.8835714
Sum1087.6631
Variance6.4990985 × 10-5
MonotonicityNot monotonic
2023-12-11T01:37:38.019697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
35.091794 3
 
9.7%
35.085311 2
 
6.5%
35.088739 1
 
3.2%
35.08771 1
 
3.2%
35.085175 1
 
3.2%
35.075558 1
 
3.2%
35.072022 1
 
3.2%
35.090399 1
 
3.2%
35.091592 1
 
3.2%
35.093635 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
35.064082 1
3.2%
35.072022 1
3.2%
35.074277 1
3.2%
35.075081 1
3.2%
35.075558 1
3.2%
35.075849 1
3.2%
35.078685 1
3.2%
35.081933 1
3.2%
35.082709 1
3.2%
35.082906 1
3.2%
ValueCountFrequency (%)
35.095856 1
 
3.2%
35.09544 1
 
3.2%
35.09469 1
 
3.2%
35.094502 1
 
3.2%
35.093635 1
 
3.2%
35.093475 1
 
3.2%
35.09245 1
 
3.2%
35.092116 1
 
3.2%
35.091794 3
9.7%
35.091592 1
 
3.2%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.05592
Minimum129.03794
Maximum129.08098
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:37:38.181587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.03794
5-th percentile129.03949
Q1129.04098
median129.0455
Q3129.07033
95-th percentile129.07783
Maximum129.08098
Range0.043045
Interquartile range (IQR)0.0293435

Descriptive statistics

Standard deviation0.015556305
Coefficient of variation (CV)0.00012053926
Kurtosis-1.7717471
Mean129.05592
Median Absolute Deviation (MAD)0.007561
Skewness0.26255057
Sum4000.7336
Variance0.00024199863
MonotonicityNot monotonic
2023-12-11T01:37:38.353621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
129.040685 3
 
9.7%
129.040333 2
 
6.5%
129.069015 1
 
3.2%
129.04474 1
 
3.2%
129.070707 1
 
3.2%
129.070363 1
 
3.2%
129.074207 1
 
3.2%
129.038646 1
 
3.2%
129.041284 1
 
3.2%
129.040386 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
129.037937 1
 
3.2%
129.038646 1
 
3.2%
129.040333 2
6.5%
129.040386 1
 
3.2%
129.040685 3
9.7%
129.041284 1
 
3.2%
129.041788 1
 
3.2%
129.04307 1
 
3.2%
129.043663 1
 
3.2%
129.04474 1
 
3.2%
ValueCountFrequency (%)
129.080982 1
3.2%
129.078442 1
3.2%
129.077222 1
3.2%
129.076356 1
3.2%
129.075385 1
3.2%
129.074207 1
3.2%
129.070707 1
3.2%
129.070363 1
3.2%
129.070293 1
3.2%
129.069015 1
3.2%

구간시점
Text

Distinct24
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T01:37:38.561541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.6451613
Min length4

Characters and Unicode

Total characters175
Distinct characters75
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)61.3%

Sample

1st row영도대교
2nd row소방서앞사거리
3rd row봉래교차로
4th row청학119
5th row영도구청
ValueCountFrequency (%)
봉래교차로 4
 
12.1%
대교사거리 2
 
6.1%
소방서앞사거리 2
 
6.1%
영선아래교차로 2
 
6.1%
항만119 2
 
6.1%
일동미라주1단지 1
 
3.0%
기업은행 1
 
3.0%
한진로즈힐 1
 
3.0%
동삼교회앞삼거리 1
 
3.0%
서경세차장 1
 
3.0%
Other values (16) 16
48.5%
2023-12-11T01:37:38.933438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 12
 
6.9%
로 8
 
4.6%
리 7
 
4.0%
차 7
 
4.0%
1 7
 
4.0%
거 6
 
3.4%
래 6
 
3.4%
영 6
 
3.4%
대 6
 
3.4%
동 5
 
2.9%
Other values (65) 105
60.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 163
93.1%
Decimal Number 10
 
5.7%
Space Separator 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 12
 
7.4%
로 8
 
4.9%
리 7
 
4.3%
차 7
 
4.3%
거 6
 
3.7%
래 6
 
3.7%
영 6
 
3.7%
대 6
 
3.7%
동 5
 
3.1%
앞 4
 
2.5%
Other values (62) 96
58.9%
Decimal Number
ValueCountFrequency (%)
1 7
70.0%
9 3
30.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 163
93.1%
Common 12
 
6.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 12
 
7.4%
로 8
 
4.9%
리 7
 
4.3%
차 7
 
4.3%
거 6
 
3.7%
래 6
 
3.7%
영 6
 
3.7%
대 6
 
3.7%
동 5
 
3.1%
앞 4
 
2.5%
Other values (62) 96
58.9%
Common
ValueCountFrequency (%)
1 7
58.3%
9 3
25.0%
2
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 163
93.1%
ASCII 12
 
6.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 12
 
7.4%
로 8
 
4.9%
리 7
 
4.3%
차 7
 
4.3%
거 6
 
3.7%
래 6
 
3.7%
영 6
 
3.7%
대 6
 
3.7%
동 5
 
3.1%
앞 4
 
2.5%
Other values (62) 96
58.9%
ASCII
ValueCountFrequency (%)
1 7
58.3%
9 3
25.0%
2
 
16.7%

구간종점
Text

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T01:37:39.156345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length5.7741935
Min length4

Characters and Unicode

Total characters179
Distinct characters81
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)93.5%

Sample

1st row소방서앞사거리
2nd row봉래교차로
3rd row청학119
4th row영도구청
5th row항만119
ValueCountFrequency (%)
소방서앞사거리 2
 
6.1%
봉래교차로 1
 
3.0%
남항대교 1
 
3.0%
고신대학교 1
 
3.0%
영도롯데캐슬 1
 
3.0%
영선윗로터리 1
 
3.0%
대교사거리 1
 
3.0%
인제요양병원 1
 
3.0%
부산대교 1
 
3.0%
신한기공사앞교차로 1
 
3.0%
Other values (22) 22
66.7%
2023-12-11T01:37:39.533584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 14
 
7.8%
대 7
 
3.9%
영 7
 
3.9%
리 6
 
3.4%
학 6
 
3.4%
로 5
 
2.8%
1 5
 
2.8%
도 5
 
2.8%
앞 4
 
2.2%
사 4
 
2.2%
Other values (71) 116
64.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 170
95.0%
Decimal Number 7
 
3.9%
Space Separator 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 14
 
8.2%
대 7
 
4.1%
영 7
 
4.1%
리 6
 
3.5%
학 6
 
3.5%
로 5
 
2.9%
도 5
 
2.9%
앞 4
 
2.4%
사 4
 
2.4%
차 4
 
2.4%
Other values (68) 108
63.5%
Decimal Number
ValueCountFrequency (%)
1 5
71.4%
9 2
 
28.6%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 170
95.0%
Common 9
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 14
 
8.2%
대 7
 
4.1%
영 7
 
4.1%
리 6
 
3.5%
학 6
 
3.5%
로 5
 
2.9%
도 5
 
2.9%
앞 4
 
2.4%
사 4
 
2.4%
차 4
 
2.4%
Other values (68) 108
63.5%
Common
ValueCountFrequency (%)
1 5
55.6%
2
 
22.2%
9 2
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 170
95.0%
ASCII 9
 
5.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 14
 
8.2%
대 7
 
4.1%
영 7
 
4.1%
리 6
 
3.5%
학 6
 
3.5%
로 5
 
2.9%
도 5
 
2.9%
앞 4
 
2.4%
사 4
 
2.4%
차 4
 
2.4%
Other values (68) 108
63.5%
ASCII
ValueCountFrequency (%)
1 5
55.6%
2
 
22.2%
9 2
 
22.2%

식재거리(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean686.45161
Minimum80
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:37:39.999373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile150
Q1200
median500
Q3950
95-th percentile1700
Maximum2500
Range2420
Interquartile range (IQR)750

Descriptive statistics

Standard deviation576.73534
Coefficient of variation (CV)0.84016897
Kurtosis2.3014532
Mean686.45161
Median Absolute Deviation (MAD)300
Skewness1.472448
Sum21280
Variance332623.66
MonotonicityNot monotonic
2023-12-11T01:37:40.132577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
200 8
25.8%
500 3
 
9.7%
800 2
 
6.5%
1400 2
 
6.5%
700 2
 
6.5%
1000 2
 
6.5%
600 2
 
6.5%
400 2
 
6.5%
80 1
 
3.2%
1200 1
 
3.2%
Other values (6) 6
19.4%
ValueCountFrequency (%)
80 1
 
3.2%
100 1
 
3.2%
200 8
25.8%
300 1
 
3.2%
400 2
 
6.5%
500 3
 
9.7%
600 2
 
6.5%
700 2
 
6.5%
800 2
 
6.5%
900 1
 
3.2%
ValueCountFrequency (%)
2500 1
3.2%
2000 1
3.2%
1400 2
6.5%
1300 1
3.2%
1200 1
3.2%
1000 2
6.5%
900 1
3.2%
800 2
6.5%
700 2
6.5%
600 2
6.5%

총합계
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.12903
Minimum4
Maximum388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:37:40.260803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9.5
Q141
median81
Q3130
95-th percentile296
Maximum388
Range384
Interquartile range (IQR)89

Descriptive statistics

Standard deviation92.201136
Coefficient of variation (CV)0.90279065
Kurtosis3.0926088
Mean102.12903
Median Absolute Deviation (MAD)48
Skewness1.6986359
Sum3166
Variance8501.0495
MonotonicityNot monotonic
2023-12-11T01:37:40.389813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
110 2
 
6.5%
60 2
 
6.5%
31 2
 
6.5%
42 2
 
6.5%
135 1
 
3.2%
62 1
 
3.2%
102 1
 
3.2%
70 1
 
3.2%
188 1
 
3.2%
171 1
 
3.2%
Other values (17) 17
54.8%
ValueCountFrequency (%)
4 1
3.2%
9 1
3.2%
10 1
3.2%
20 1
3.2%
21 1
3.2%
31 2
6.5%
40 1
3.2%
42 2
6.5%
47 1
3.2%
60 2
6.5%
ValueCountFrequency (%)
388 1
3.2%
350 1
3.2%
242 1
3.2%
188 1
3.2%
178 1
3.2%
171 1
3.2%
135 1
3.2%
131 1
3.2%
129 1
3.2%
121 1
3.2%

왕벚나무
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing24
Missing (%)77.4%
Infinite0
Infinite (%)0.0%
Mean97.857143
Minimum4
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:37:40.503414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9.1
Q131.5
median121
Q3154.5
95-th percentile185
Maximum188
Range184
Interquartile range (IQR)123

Descriptive statistics

Standard deviation75.298138
Coefficient of variation (CV)0.76947002
Kurtosis-2.0465817
Mean97.857143
Median Absolute Deviation (MAD)67
Skewness-0.086927323
Sum685
Variance5669.8095
MonotonicityNot monotonic
2023-12-11T01:37:40.598868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
131 1
 
3.2%
121 1
 
3.2%
178 1
 
3.2%
42 1
 
3.2%
4 1
 
3.2%
21 1
 
3.2%
188 1
 
3.2%
(Missing) 24
77.4%
ValueCountFrequency (%)
4 1
3.2%
21 1
3.2%
42 1
3.2%
121 1
3.2%
131 1
3.2%
178 1
3.2%
188 1
3.2%
ValueCountFrequency (%)
188 1
3.2%
178 1
3.2%
131 1
3.2%
121 1
3.2%
42 1
3.2%
21 1
3.2%
4 1
3.2%

은행나무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
<NA>
27 
40
 
1
9
 
1
62
 
1
47
 
1

Length

Max length4
Median length4
Mean length3.7096774
Min length1

Unique

Unique4 ?
Unique (%)12.9%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 27
87.1%
40 1
 
3.2%
9 1
 
3.2%
62 1
 
3.2%
47 1
 
3.2%

Length

2023-12-11T01:37:40.714460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:37:40.810592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 27
87.1%
40 1
 
3.2%
9 1
 
3.2%
62 1
 
3.2%
47 1
 
3.2%

느티나무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size380.0 B
<NA>
28 
86
 
1
70
 
1
102
 
1

Length

Max length4
Median length4
Mean length3.8387097
Min length2

Unique

Unique3 ?
Unique (%)9.7%

Sample

1st row<NA>
2nd row86
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 28
90.3%
86 1
 
3.2%
70 1
 
3.2%
102 1
 
3.2%

Length

2023-12-11T01:37:40.921508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:37:41.028811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
90.3%
86 1
 
3.2%
70 1
 
3.2%
102 1
 
3.2%

이팝나무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size380.0 B
<NA>
25 
31
 
2
135
 
1
242
 
1
129
 
1

Length

Max length4
Median length4
Mean length3.7419355
Min length2

Unique

Unique4 ?
Unique (%)12.9%

Sample

1st row135
2nd row<NA>
3rd row<NA>
4th row242
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 25
80.6%
31 2
 
6.5%
135 1
 
3.2%
242 1
 
3.2%
129 1
 
3.2%
171 1
 
3.2%

Length

2023-12-11T01:37:41.140825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:37:41.249331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 25
80.6%
31 2
 
6.5%
135 1
 
3.2%
242 1
 
3.2%
129 1
 
3.2%
171 1
 
3.2%

후박나무
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing24
Missing (%)77.4%
Infinite0
Infinite (%)0.0%
Mean89.428571
Minimum10
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:37:41.345395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12.4
Q119
median58
Q385
95-th percentile278
Maximum350
Range340
Interquartile range (IQR)66

Descriptive statistics

Standard deviation120.00397
Coefficient of variation (CV)1.3418974
Kurtosis5.1783582
Mean89.428571
Median Absolute Deviation (MAD)40
Skewness2.220242
Sum626
Variance14400.952
MonotonicityNot monotonic
2023-12-11T01:37:41.447054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
110 1
 
3.2%
18 1
 
3.2%
350 1
 
3.2%
58 1
 
3.2%
20 1
 
3.2%
60 1
 
3.2%
10 1
 
3.2%
(Missing) 24
77.4%
ValueCountFrequency (%)
10 1
3.2%
18 1
3.2%
20 1
3.2%
58 1
3.2%
60 1
3.2%
110 1
3.2%
350 1
3.2%
ValueCountFrequency (%)
350 1
3.2%
110 1
3.2%
60 1
3.2%
58 1
3.2%
20 1
3.2%
18 1
3.2%
10 1
3.2%

먼나무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
<NA>
27 
110
 
1
77
 
1
388
 
1
2
 
1

Length

Max length4
Median length4
Mean length3.7741935
Min length1

Unique

Unique4 ?
Unique (%)12.9%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 27
87.1%
110 1
 
3.2%
77 1
 
3.2%
388 1
 
3.2%
2 1
 
3.2%

Length

2023-12-11T01:37:41.569247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:37:41.664239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 27
87.1%
110 1
 
3.2%
77 1
 
3.2%
388 1
 
3.2%
2 1
 
3.2%

해송
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
<NA>
30 
42
 
1

Length

Max length4
Median length4
Mean length3.9354839
Min length2

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 30
96.8%
42 1
 
3.2%

Length

2023-12-11T01:37:41.770583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:37:41.865550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 30
96.8%
42 1
 
3.2%

가시나무
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
<NA>
30 
81
 
1

Length

Max length4
Median length4
Mean length3.9354839
Min length2

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 30
96.8%
81 1
 
3.2%

Length

2023-12-11T01:37:41.980817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:37:42.086098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 30
96.8%
81 1
 
3.2%

구군명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
부산광역시 영도구
31 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 영도구
2nd row부산광역시 영도구
3rd row부산광역시 영도구
4th row부산광역시 영도구
5th row부산광역시 영도구

Common Values

ValueCountFrequency (%)
부산광역시 영도구 31
100.0%

Length

2023-12-11T01:37:42.175895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:37:42.265278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 31
50.0%
영도구 31
50.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
2022-12-26
31 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-26
2nd row2022-12-26
3rd row2022-12-26
4th row2022-12-26
5th row2022-12-26

Common Values

ValueCountFrequency (%)
2022-12-26 31
100.0%

Length

2023-12-11T01:37:42.369272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:37:42.463211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-26 31
100.0%

Interactions

2023-12-11T01:37:35.834610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:32.667413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:33.490019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:34.082240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:34.637665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:35.273591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:35.927587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:32.751611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:33.578308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:34.172423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:34.726415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:35.374078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:36.060531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:32.838348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:33.673353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:34.250255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:34.812890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:35.466106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:36.159983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:32.916841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:33.765171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:34.328653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:34.917706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:35.558000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:36.269209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:33.268207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:33.880814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:34.437751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:35.023074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:35.662154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:36.356039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:33.398987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:33.988689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:34.553543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:35.146938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:35.753216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:37:42.535036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치명위도경도구간시점구간종점식재거리(km)총합계왕벚나무은행나무느티나무이팝나무후박나무먼나무
위치명1.0000.0000.0000.8700.9870.0000.0001.0001.0001.0000.8590.0001.000
위도0.0001.0000.5150.8700.8760.0000.0000.8881.0001.0001.0000.8711.000
경도0.0000.5151.0000.9180.9510.5630.8000.0001.0001.0000.9420.7081.000
구간시점0.8700.8700.9181.0000.9740.7220.9171.0001.0001.0001.0000.6421.000
구간종점0.9870.8760.9510.9741.0001.0000.8941.0001.0001.0001.0001.0001.000
식재거리(km)0.0000.0000.5630.7221.0001.0000.7350.9281.0001.0001.0000.8461.000
총합계0.0000.0000.8000.9170.8940.7351.0001.0001.0001.0001.0000.9741.000
왕벚나무1.0000.8880.0001.0001.0000.9281.0001.000NaNNaNNaNNaNNaN
은행나무1.0001.0001.0001.0001.0001.0001.000NaN1.000NaNNaNNaNNaN
느티나무1.0001.0001.0001.0001.0001.0001.000NaNNaN1.000NaNNaNNaN
이팝나무0.8591.0000.9421.0001.0001.0001.000NaNNaNNaN1.000NaNNaN
후박나무0.0000.8710.7080.6421.0000.8460.974NaNNaNNaNNaN1.0000.000
먼나무1.0001.0001.0001.0001.0001.0001.000NaNNaNNaNNaN0.0001.000
2023-12-11T01:37:42.745683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이팝나무느티나무먼나무가시나무은행나무해송
이팝나무1.000NaNNaNNaNNaNNaN
느티나무NaN1.000NaNNaNNaNNaN
먼나무NaNNaN1.000NaNNaNNaN
가시나무NaNNaNNaN1.000NaNNaN
은행나무NaNNaNNaNNaN1.000NaN
해송NaNNaNNaNNaNNaN1.000
2023-12-11T01:37:42.927313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도식재거리(km)총합계왕벚나무후박나무은행나무느티나무이팝나무먼나무해송가시나무
위도1.000-0.5550.027-0.058-0.5000.1791.0001.0001.0001.000NaNNaN
경도-0.5551.0000.0340.2880.5360.7141.0001.0000.2501.000NaNNaN
식재거리(km)0.0270.0341.0000.7940.9290.4501.0001.0001.0001.000NaNNaN
총합계-0.0580.2880.7941.0001.0000.7751.0001.0000.8661.000NaNNaN
왕벚나무-0.5000.5360.9291.0001.000NaN0.0000.0000.0000.0000.0000.000
후박나무0.1790.7140.4500.775NaN1.0000.0000.0000.0001.0000.0000.000
은행나무1.0001.0001.0001.0000.0000.0001.0000.0000.0000.0000.0000.000
느티나무1.0001.0001.0001.0000.0000.0000.0001.0000.0000.0000.0000.000
이팝나무1.0000.2501.0000.8660.0000.0000.0000.0001.0000.0000.0000.000
먼나무1.0001.0001.0001.0000.0001.0000.0000.0000.0001.0000.0000.000
해송NaNNaNNaNNaN0.0000.0000.0000.0000.0000.0001.0000.000
가시나무NaNNaNNaNNaN0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-11T01:37:36.526290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:37:36.814985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T01:37:37.008152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

위치명위도경도구간시점구간종점식재거리(km)총합계왕벚나무은행나무느티나무이팝나무후박나무먼나무해송가시나무구군명데이터기준일자
0부산광역시 영도구 태종로35.088739129.069015영도대교소방서앞사거리800135<NA><NA><NA>135<NA><NA><NA><NA>부산광역시 영도구2022-12-26
1부산광역시 영도구 태종로35.082906129.076356소방서앞사거리봉래교차로20086<NA><NA>86<NA><NA><NA><NA><NA>부산광역시 영도구2022-12-26
2부산광역시 영도구 태종로35.064082129.080982봉래교차로청학1191400110<NA><NA><NA><NA>110<NA><NA><NA>부산광역시 영도구2022-12-26
3부산광역시 영도구 태종로35.095856129.053289청학119영도구청1400242<NA><NA><NA>242<NA><NA><NA><NA>부산광역시 영도구2022-12-26
4부산광역시 영도구 태종로35.091794129.040685영도구청항만119700131131<NA><NA><NA><NA><NA><NA><NA>부산광역시 영도구2022-12-26
5부산광역시 영도구 태종로35.091794129.040685항만119해경교차로1000110<NA><NA><NA><NA><NA>110<NA><NA>부산광역시 영도구2022-12-26
6부산광역시 영도구 태종로35.091794129.040685동삼동패총태종대입구70095<NA><NA><NA><NA>1877<NA><NA>부산광역시 영도구2022-12-26
7부산광역시 영도구 해양로35.083321129.077222해양대삼거리미창석유2000388<NA><NA><NA><NA><NA>388<NA><NA>부산광역시 영도구2022-12-26
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9부산광역시 영도구 해양로35.078685129.078442영도마린축구장국립해양박물관500129<NA><NA><NA>129<NA><NA><NA><NA>부산광역시 영도구2022-12-26
위치명위도경도구간시점구간종점식재거리(km)총합계왕벚나무은행나무느티나무이팝나무후박나무먼나무해송가시나무구군명데이터기준일자
21부산광역시 영도구 산업로35.094502129.065924대성주유소신한기공사앞교차로40060<NA><NA><NA><NA>60<NA><NA><NA>부산광역시 영도구2022-12-26
22부산광역시 영도구 대교로35.093475129.04307봉래교차로부산대교50081<NA><NA><NA><NA><NA><NA><NA>81부산광역시 영도구2022-12-26
23부산광역시 영도구 절영로35.093635129.040386서경세차장인제요양병원20047<NA>47<NA><NA><NA><NA><NA><NA>부산광역시 영도구2022-12-26
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25부산광역시 영도구 절영로35.090399129.038646대교사거리영선윗로터리1000171<NA><NA><NA>171<NA><NA><NA><NA>부산광역시 영도구2022-12-26
26부산광역시 영도구 와치로35.072022129.074207동삼교회앞삼거리영도롯데캐슬20042<NA><NA><NA><NA><NA><NA>42<NA>부산광역시 영도구2022-12-26
27부산광역시 영도구 와치로35.075558129.070363한진로즈힐고신대학교900188188<NA><NA><NA><NA><NA><NA><NA>부산광역시 영도구2022-12-26
28부산광역시 영도구 영선대로35.085311129.040333영선아래교차로남항대교120031<NA><NA><NA>31<NA><NA><NA><NA>부산광역시 영도구2022-12-26
29부산광역시 영도구 영선대로35.085311129.040333영선아래교차로소방서앞사거리60070<NA><NA>70<NA><NA><NA><NA><NA>부산광역시 영도구2022-12-26
30부산광역시 영도구 상리로35.085175129.070707항만119상리초등학교500102<NA><NA>102<NA><NA><NA><NA><NA>부산광역시 영도구2022-12-26